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C++17 In Detail, a book that will move you from C++11/14 to C++17

Hacker News

C++17 In Detail, a book that will move you from C++11/14 to C++17

Hi! Last year in August I've released my first self-published book. C++17 In Detail Available at Leanpub: https://leanpub.com/cpp17indetail Keeping up with the latest changes in the language is quite hard. C++ moves quite fast recently, and there are many exciting features added with each iteration. The book shows all the significant changes in the language and the Standard Library. The chapters are much easier to grasp than reading reference pages or the Standard itself. It's a great guide for any C++ developer who knows a little about C++11/14 and wants to move forward. In August the book was 90% ready, and I worked since then to write the last chapters and improve the content. In March it reached finally the 100% status :) I would appreciate any feedback! You can also ask me about the publishing process.

Visit product View on Hacker NewsMar 14, 2019

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73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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